Bayesian segmentation of brainstem structures in MRI

VK: Lampinen, J.In this paper we present a method to segment four brainstem structures (midbrain, pons, medulla oblongata and superior cerebellar peduncle) from 3D brain MRI scans. The segmentation method relies on a probabilistic atlas of the brainstem and its neighboring brain structures. To build the atlas, we combined a dataset of 39 scans with already existing manual delineations of the whole brainstem and a dataset of 10 scans in which the brainstem structures were manually labeled with a protocol that was specifically designed for this study. The resulting atlas can be used in a Bayesian framework to segment the brainstem structures in novel scans. Thanks to the generative nature of the scheme, the segmentation method is robust to changes in MRI contrast or acquisition hardware. Using cross validation, we show that the algorithm can segment the structures in previously unseen T1 and FLAIR scans with great accuracy (mean error under 1 mm) and robustness (no failures in 383 scans including 168 AD cases). We also indirectly evaluate the algorithm with a experiment in which we study the atrophy of the brainstem in aging. The results show that, when used simultaneously, the volumes of the midbrain, pons and medulla are significantly more predictive of age than the volume of the entire brainstem, estimated as their sum. The results also demonstrate that the method can detect atrophy patterns in the brainstem structures that have been previously described in the literature. Finally, we demonstrate that the proposed algorithm is able to detect differential effects of AD on the brainstem structures. The method will be implemented as part of the popular neuroimaging package FreeSurfer.Peer reviewe

White matter volume assessment in premature infants on MRI at term - computer aided volume analysis

The objective of this study is the development of an automatic segmentation framework for measuring volume changes in the white matter tissue from premature infant MRI data. The early stage of the brain development presents several major computational challenges such as structure and shape variations between patients. Furthermore, a high water content is present in the brain tissue, that leads to inconsistencies and overlapping intensity values across different brain structures. Another problem lies in low-frequency multiplicative intensity variations, which arises from an inhomogeneous magnetic field during the MRI acquisition. Finally, the segmentation is influenced by the partial volume effects which describe voxels that are generated by more than one tissue type. To overcome these challenges, this study is divided into three parts with the intention to locally segment the white matter tissue without the guidance of an atlas. Firstly, a novel brain extraction method is proposed with the aim to remove all non-brain tissue. The data quality can be improved by noise reduction using an anisotropic diffusion filter and intensity variations adjustments throughout the volume. In order to minimise the influence of missing contours and overlapping intensity values between brain and nonbrain tissue, a brain mask is created and applied during the extraction of the brain tissue. Secondly, the low-frequency intensity inhomogeneities are addressed by calculating the bias field which can be separated and corrected using low pass filtering. Finally, the segmentation process is performed by combining probabilistic clustering with classification algorithms. In order to achieve the final segmentation, the algorithm starts with a pre-segmentation procedure which was applied to reduce the intensity inhomogeneities within the white matter tissue. The key element in the segmentation process is the classification of diffused and missing contours as well as the partial volume voxels by performing a voxel reclassification scheme. The white matter segmentation framework was tested using the Dice Similarity Metric, and the numerical evaluation demonstrated precise segmentation results